Changes in Nursing Team Composition and Risk of Device Associated Infection in Intensive Care Units. (October 2020)

• Record Type: Journal Article
• Authors: Shah, Hirsh; Knowlson, Shelley A; Roberson, Audrey; Godbout, Emily; Stevens, Michael; Bearman, Gonzalo; Doll, Michelle Elizabeth
• Is Part Of: Infection control and hospital epidemiology. Volume 41(2020)Supplement 1
• Page Start: s465
• Page End: s465
• Publication Date: 2020-10
• Subjects: Nosocomial infections -- Epidemiology -- Periodicals; Health facilities -- Sanitation -- Periodicals; Hospital buildings -- Sanitation -- Periodicals; Cross Infection -- Periodicals; Epidemiology -- Periodicals; Hospitals -- Periodicals; Infection Control -- Periodicals; 614.44
• DOI: 10.1017/ice.2020.1139
• Languages: English
• ISSNs: 0899-823X

Abstract

Abstract : Background: The relationship between nursing staffing and healthcare-associated infections (HAIs) has been explored previously, with conflicting results. Intensive care units increasingly struggle to maintain trained staff. In May 2019, clinical coordinator (CC) roles changed to include 50% of time in direct patient care rather than supportive roles. In this study, we used shift records to explore the impact of staffing on HAI risk. Methods: Daily staffing records from December 2018 August 2019 for the medical-respiratory unit (MRICU) and the cardiac surgery unit (CSICU) were reviewed. Both units staff a fixed 2:1 patient:nurse ratio (1:1 for specific cardiac surgeries). Staff deficiency was defined as assignments filled by nurses pulled from other units/supplemental/or CC roles. Staff support comprised nursing assistants and unit secretaries. Census, admissions, and complexity score for number of devices were used to estimate care acuity. In CSICU, additional points were added for continuous renal replacement therapy, extracorporeal membrane oxygenation, ventricular assist devices, transplant, operative cases. NHSN definitions were used for central-line–associated bloodstream infections (CLABSIs) and catheter-associated urinary tract infections (CAUTIs). The Spearman correlation coefficient was used to determine relationship between staffing, acuity, and risk window for HAI (days 1–10 preinfection). Linear regression was used to determine whether staffing deficiencies and/or support associate with the risk window prior to HAI. The final model included census and complexity score as control variables. The statistical analysis was performed using SAS version 9.4 software (Cary, NC). Results: Overall, 8 HAIs occurred in the study period: medical-respiratory intensive care unit (MRICU: 3 CAUTIs and 1 CLABSI) and cardiac surgery intensive care unit (CSICU: 1 CAUTI and 3 CLABSIs). Staffing and census fluctuated daily (Table 1). Total number of nurses correlated with complexity scores (r = 0.35; P < .0001) and daily census (r = 0.31; P < .0001) in the CSICU, and the census (r = 0.12; P = .04) in the MRICU. Nursing deficiencies correlated with days 1–10 before infection (r = 0.20; P = .0013) in the CSICU. In the regression model for the CSICU, nursing deficiencies increased in the time prior to HAI ( P = .004), and support staff decreased in the time prior to HAI ( P = .034) while controlling for census and complexity. These relationships were not significant in the MRICU. Conclusion: The lack of core nurses to support the staffing structure in CSICU correlated with periods prior to CLABSI or CAUTI in this small, unit-based study. Failure to recruit and retain highly skilled core staff may produce HAI risks, particularly for CLABSI in specialized units. Funding: None Disclosures: Michelle Doll, Research Grant from Molnlycke Healthcare